Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index

Abstract : In this paper, we propose a new clustering method based on the combination of K-harmonic means (KHM) clustering algorithm and cluster validity index for remotely sensed data clustering. The KHM is essentially insensitive to the initialization of the centers. In addition, cluster validity index is introduced to determine the optimal number of clusters in the data studied. Four cluster validity indices were compared in this work namely, DB index, XB index, PBMF index, WB-index and a new index has been deduced namely, WXI. The Experimental results and comparison with both K-means (KM) and fuzzy C-means (FCM) algorithms confirm the effectiveness of the proposed methodology.
Document type :
Conference papers
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download

https://hal.inria.fr/hal-01789935
Contributor : Hal Ifip <>
Submitted on : Friday, May 11, 2018 - 3:10:05 PM
Last modification on : Wednesday, October 31, 2018 - 11:06:08 AM
Long-term archiving on : Tuesday, September 25, 2018 - 9:29:21 AM

File

339159_1_En_9_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Habib Mahi, Nezha Farhi, Kaouter Labed. Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index. 5th International Conference on Computer Science and Its Applications (CIIA), May 2015, Saida, Algeria. pp.105-116, ⟨10.1007/978-3-319-19578-0_9⟩. ⟨hal-01789935⟩

Share

Metrics

Record views

357

Files downloads

89